Abstract
Tables are recognized for their high information density and widespread usage, serving as essential sources of information. Seeking information from tables (TIS) is a crucial capability for Large Language Models (LLMs), serving as the foundation of knowledge-based Q&A systems. However, this field presently suffers from an absence of thorough and reliable evaluation. This paper introduces a more reliable benchmark for Table Information Seeking (TabIS). To avoid the unreliable evaluation caused by text similarity-based metrics, TabIS adopts a single-choice question format (with two options per question) instead of a text generation format. We establish an effective pipeline for generating options, ensuring their difficulty and quality. Experiments conducted on 12 LLMs reveal that while the performance of GPT-4-turbo is marginally satisfactory, both other proprietary and open-source models perform inadequately. Further analysis shows that LLMs exhibit a poor understanding of table structures, and struggle to balance between TIS performance and robustness against pseudo-relevant tables (common in retrieval-augmented systems). These findings uncover the limitations and potential challenges of LLMs in seeking information from tables. We release our data and code to facilitate further research in this field.- Anthology ID:
- 2024.findings-acl.82
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2024
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Lun-Wei Ku, Andre Martins, Vivek Srikumar
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1388–1409
- Language:
- URL:
- https://aclanthology.org/2024.findings-acl.82
- DOI:
- 10.18653/v1/2024.findings-acl.82
- Cite (ACL):
- Chaoxu Pang, Yixuan Cao, Chunhao Yang, and Ping Luo. 2024. Uncovering Limitations of Large Language Models in Information Seeking from Tables. In Findings of the Association for Computational Linguistics: ACL 2024, pages 1388–1409, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- Uncovering Limitations of Large Language Models in Information Seeking from Tables (Pang et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/autopr/2024.findings-acl.82.pdf